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# ==================================================================== #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
# Center Groningen in The Netherlands, in collaboration with many #
# colleagues from around the world, see our website. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Calculate Antimicrobial Resistance
#'
#' @description These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in `summarise()` from the `dplyr` package and also support grouped variables, see *Examples*.
#'
#' [resistance()] should be used to calculate resistance, [susceptibility()] should be used to calculate susceptibility.\cr
#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with [as.sir()] if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See *Examples*.
#' @param minimum the minimum allowed number of available (tested) isolates. Any isolate count lower than `minimum` will return `NA` with a warning. The default number of `30` isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see *Source*.
#' @param as_percent a [logical] to indicate whether the output must be returned as a hundred fold with % sign (a character). A value of `0.123456` will then be returned as `"12.3%"`.
#' @param only_all_tested (for combination therapies, i.e. using more than one variable for `...`): a [logical] to indicate that isolates must be tested for all antibiotics, see section *Combination Therapy* below
#' @param data a [data.frame] containing columns with class [`sir`] (see [as.sir()])
#' @param translate_ab a column name of the [antibiotics] data set to translate the antibiotic abbreviations to, using [ab_property()]
#' @inheritParams ab_property
#' @param combine_SI a [logical] to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant) - the default is `TRUE`
#' @param ab_result antibiotic results to test against, must be one or more values of "S", "I", or "R"
#' @param confidence_level the confidence level for the returned confidence interval. For the calculation, the number of S or SI isolates, and R isolates are compared with the total number of available isolates with R, S, or I by using [binom.test()], i.e., the Clopper-Pearson method.
#' @param side the side of the confidence interval to return. The default is `"both"` for a length 2 vector, but can also be (abbreviated as) `"min"`/`"left"`/`"lower"`/`"less"` or `"max"`/`"right"`/`"higher"`/`"greater"`.
#' @param collapse a [logical] to indicate whether the output values should be 'collapsed', i.e. be merged together into one value, or a character value to use for collapsing
#' @inheritSection as.sir Interpretation of SIR
#' @details
#' **Remember that you should filter your data to let it contain only first isolates!** This is needed to exclude duplicates and to reduce selection bias. Use [first_isolate()] to determine them in your data set with one of the four available algorithms.
#'
#' The function [resistance()] is equal to the function [proportion_R()]. The function [susceptibility()] is equal to the function [proportion_SI()]. Since AMR v3.0, [proportion_SI()] and [proportion_I()] include dose-dependent susceptibility ('SDD').
#'
#' Use [sir_confidence_interval()] to calculate the confidence interval, which relies on [binom.test()], i.e., the Clopper-Pearson method. This function returns a vector of length 2 at default for antimicrobial *resistance*. Change the `side` argument to "left"/"min" or "right"/"max" to return a single value, and change the `ab_result` argument to e.g. `c("S", "I")` to test for antimicrobial *susceptibility*, see Examples.
#'
#' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the [`count_*()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to [count_susceptible()]` / `[count_all()]. *Low counts can influence the outcome - the `proportion_*()` functions may camouflage this, since they only return the proportion (albeit dependent on the `minimum` argument).*
#'
#' The function [proportion_df()] takes any variable from `data` that has an [`sir`] class (created with [as.sir()]) and calculates the proportions S, I, and R. It also supports grouped variables. The function [sir_df()] works exactly like [proportion_df()], but adds the number of isolates.
#' @section Combination Therapy:
#' When using more than one variable for `...` (= combination therapy), use `only_all_tested` to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Drug A and Drug B, about how [susceptibility()] works to calculate the %SI:
#'
#'
#' ```
#' --------------------------------------------------------------------
#' only_all_tested = FALSE only_all_tested = TRUE
#' ----------------------- -----------------------
#' Drug A Drug B include as include as include as include as
#' numerator denominator numerator denominator
#' -------- -------- ---------- ----------- ---------- -----------
#' S or I S or I X X X X
#' R S or I X X X X
#' <NA> S or I X X - -
#' S or I R X X X X
#' R R - X - X
#' <NA> R - - - -
#' S or I <NA> X X - -
#' R <NA> - - - -
#' <NA> <NA> - - - -
#' --------------------------------------------------------------------
#' ```
#'
#' Please note that, in combination therapies, for `only_all_tested = TRUE` applies that:
#'
#' ```
#' count_S() + count_I() + count_R() = count_all()
#' proportion_S() + proportion_I() + proportion_R() = 1
#' ```
#'
#' and that, in combination therapies, for `only_all_tested = FALSE` applies that:
#'
#' ```
#' count_S() + count_I() + count_R() >= count_all()
#' proportion_S() + proportion_I() + proportion_R() >= 1
#' ```
#'
#' Using `only_all_tested` has no impact when only using one antibiotic as input.
#' @source **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition**, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' @seealso [AMR::count()] to count resistant and susceptible isolates.
#' @return A [double] or, when `as_percent = TRUE`, a [character].
#' @rdname proportion
#' @aliases portion
#' @name proportion
#' @export
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' # run ?example_isolates for more info.
#' example_isolates
#'
#'
#' # base R ------------------------------------------------------------
#' # determines %R
#' resistance(example_isolates$AMX)
#' sir_confidence_interval(example_isolates$AMX)
#' sir_confidence_interval(example_isolates$AMX,
#' confidence_level = 0.975
#' )
#' sir_confidence_interval(example_isolates$AMX,
#' confidence_level = 0.975,
#' collapse = ", "
#' )
#'
#' # determines %S+I:
#' susceptibility(example_isolates$AMX)
#' sir_confidence_interval(example_isolates$AMX,
#' ab_result = c("S", "I")
#' )
#'
#' # be more specific
#' proportion_S(example_isolates$AMX)
#' proportion_SI(example_isolates$AMX)
#' proportion_I(example_isolates$AMX)
#' proportion_IR(example_isolates$AMX)
#' proportion_R(example_isolates$AMX)
#'
#' # dplyr -------------------------------------------------------------
#' \donttest{
#' if (require("dplyr")) {
#' example_isolates %>%
#' group_by(ward) %>%
#' summarise(
#' r = resistance(CIP),
#' n = n_sir(CIP)
#' ) # n_sir works like n_distinct in dplyr, see ?n_sir
#' }
#' if (require("dplyr")) {
#' example_isolates %>%
#' group_by(ward) %>%
#' summarise(
#' cipro_R = resistance(CIP),
#' ci_min = sir_confidence_interval(CIP, side = "min"),
#' ci_max = sir_confidence_interval(CIP, side = "max"),
#' )
#' }
#' if (require("dplyr")) {
#' # scoped dplyr verbs with antibiotic selectors
#' # (you could also use across() of course)
#' example_isolates %>%
#' group_by(ward) %>%
#' summarise_at(
#' c(aminoglycosides(), carbapenems()),
#' resistance
#' )
#' }
#' if (require("dplyr")) {
#' example_isolates %>%
#' group_by(ward) %>%
#' summarise(
#' R = resistance(CIP, as_percent = TRUE),
#' SI = susceptibility(CIP, as_percent = TRUE),
#' n1 = count_all(CIP), # the actual total; sum of all three
#' n2 = n_sir(CIP), # same - analogous to n_distinct
#' total = n()
#' ) # NOT the number of tested isolates!
#'
#' # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
#' # so we can see that combination therapy does a lot more than mono therapy:
#' example_isolates %>% susceptibility(AMC) # %SI = 76.3%
#' example_isolates %>% count_all(AMC) # n = 1879
#'
#' example_isolates %>% susceptibility(GEN) # %SI = 75.4%
#' example_isolates %>% count_all(GEN) # n = 1855
#'
#' example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%
#' example_isolates %>% count_all(AMC, GEN) # n = 1939
#'
#'
#' # See Details on how `only_all_tested` works. Example:
#' example_isolates %>%
#' summarise(
#' numerator = count_susceptible(AMC, GEN),
#' denominator = count_all(AMC, GEN),
#' proportion = susceptibility(AMC, GEN)
#' )
#'
#' example_isolates %>%
#' summarise(
#' numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
#' denominator = count_all(AMC, GEN, only_all_tested = TRUE),
#' proportion = susceptibility(AMC, GEN, only_all_tested = TRUE)
#' )
#'
#'
#' example_isolates %>%
#' group_by(ward) %>%
#' summarise(
#' cipro_p = susceptibility(CIP, as_percent = TRUE),
#' cipro_n = count_all(CIP),
#' genta_p = susceptibility(GEN, as_percent = TRUE),
#' genta_n = count_all(GEN),
#' combination_p = susceptibility(CIP, GEN, as_percent = TRUE),
#' combination_n = count_all(CIP, GEN)
#' )
#'
#' # Get proportions S/I/R immediately of all sir columns
#' example_isolates %>%
#' select(AMX, CIP) %>%
#' proportion_df(translate = FALSE)
#'
#' # It also supports grouping variables
#' # (use sir_df to also include the count)
#' example_isolates %>%
#' select(ward, AMX, CIP) %>%
#' group_by(ward) %>%
#' sir_df(translate = FALSE)
#' }
#' }
resistance <- function(...,
minimum = 30,
as_percent = FALSE,
only_all_tested = FALSE) {
tryCatch(
sir_calc(...,
ab_result = "R",
minimum = minimum,
as_percent = as_percent,
only_all_tested = only_all_tested,
only_count = FALSE
),
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
}
#' @rdname proportion
#' @export
susceptibility <- function(...,
minimum = 30,
as_percent = FALSE,
only_all_tested = FALSE) {
tryCatch(
sir_calc(...,
ab_result = c("S", "SDD", "I"),
minimum = minimum,
as_percent = as_percent,
only_all_tested = only_all_tested,
only_count = FALSE
),
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
}
#' @rdname proportion
#' @export
sir_confidence_interval <- function(...,
ab_result = "R",
minimum = 30,
as_percent = FALSE,
only_all_tested = FALSE,
confidence_level = 0.95,
side = "both",
collapse = FALSE) {
meet_criteria(ab_result, allow_class = c("character", "sir"), has_length = c(1:5), is_in = c("S", "SDD", "I", "R", "NI"))
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
meet_criteria(as_percent, allow_class = "logical", has_length = 1)
meet_criteria(only_all_tested, allow_class = "logical", has_length = 1)
meet_criteria(confidence_level, allow_class = "numeric", is_positive = TRUE, has_length = 1)
meet_criteria(side, allow_class = "character", has_length = 1, is_in = c("both", "b", "left", "l", "lower", "lowest", "less", "min", "right", "r", "higher", "highest", "greater", "g", "max"))
meet_criteria(collapse, allow_class = c("logical", "character"), has_length = 1)
x <- tryCatch(
sir_calc(...,
ab_result = ab_result,
only_all_tested = only_all_tested,
only_count = TRUE
),
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
n <- tryCatch(
sir_calc(...,
ab_result = c("S", "SDD", "I", "R", "NI"),
only_all_tested = only_all_tested,
only_count = TRUE
),
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
# this applies the Clopper-Pearson method
out <- stats::binom.test(x = x, n = n, conf.level = confidence_level)$conf.int
out <- set_clean_class(out, "numeric")
if (side %in% c("left", "l", "lower", "lowest", "less", "min")) {
out <- out[1]
} else if (side %in% c("right", "r", "higher", "highest", "greater", "g", "max")) {
out <- out[2]
}
if (isTRUE(as_percent)) {
out <- percentage(out, digits = 1)
}
if (!isFALSE(collapse) && length(out) > 1) {
if (is.numeric(out)) {
out <- round(out, digits = 3)
}
out <- paste(out, collapse = ifelse(isTRUE(collapse), "-", collapse))
}
if (n < minimum) {
warning_("Introducing NA: ",
ifelse(n == 0, "no", paste("only", n)),
" results available for `sir_confidence_interval()` (`minimum` = ", minimum, ").",
call = FALSE
)
if (is.character(out)) {
return(NA_character_)
} else {
return(NA_real_)
}
}
out
}
#' @rdname proportion
#' @export
proportion_R <- function(...,
minimum = 30,
as_percent = FALSE,
only_all_tested = FALSE) {
tryCatch(
sir_calc(...,
ab_result = "R",
minimum = minimum,
as_percent = as_percent,
only_all_tested = only_all_tested,
only_count = FALSE
),
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
}
#' @rdname proportion
#' @export
proportion_IR <- function(...,
minimum = 30,
as_percent = FALSE,
only_all_tested = FALSE) {
if (message_not_thrown_before("proportion_IR", entire_session = TRUE)) {
message_("Note that `proportion_IR()` will also include dose-dependent susceptibility, 'SDD'. This note will be shown once for this session.", as_note = FALSE)
}
tryCatch(
sir_calc(...,
ab_result = c("I", "SDD", "R"),
minimum = minimum,
as_percent = as_percent,
only_all_tested = only_all_tested,
only_count = FALSE
),
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
}
#' @rdname proportion
#' @export
proportion_I <- function(...,
minimum = 30,
as_percent = FALSE,
only_all_tested = FALSE) {
if (message_not_thrown_before("proportion_I", entire_session = TRUE)) {
message_("Note that `proportion_I()` will also include dose-dependent susceptibility, 'SDD'. This note will be shown once for this session.", as_note = FALSE)
}
tryCatch(
sir_calc(...,
ab_result = c("I", "SDD"),
minimum = minimum,
as_percent = as_percent,
only_all_tested = only_all_tested,
only_count = FALSE
),
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
}
#' @rdname proportion
#' @export
proportion_SI <- function(...,
minimum = 30,
as_percent = FALSE,
only_all_tested = FALSE) {
if (message_not_thrown_before("proportion_SI", entire_session = TRUE)) {
message_("Note that `proportion_SI()` will also include dose-dependent susceptibility, 'SDD'. This note will be shown once for this session.", as_note = FALSE)
}
tryCatch(
sir_calc(...,
ab_result = c("S", "I", "SDD"),
minimum = minimum,
as_percent = as_percent,
only_all_tested = only_all_tested,
only_count = FALSE
),
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
}
#' @rdname proportion
#' @export
proportion_S <- function(...,
minimum = 30,
as_percent = FALSE,
only_all_tested = FALSE) {
tryCatch(
sir_calc(...,
ab_result = "S",
minimum = minimum,
as_percent = as_percent,
only_all_tested = only_all_tested,
only_count = FALSE
),
error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
)
}
#' @rdname proportion
#' @export
proportion_df <- function(data,
translate_ab = "name",
language = get_AMR_locale(),
minimum = 30,
as_percent = FALSE,
combine_SI = TRUE,
confidence_level = 0.95) {
tryCatch(
sir_calc_df(
type = "proportion",
data = data,
translate_ab = translate_ab,
language = language,
minimum = minimum,
as_percent = as_percent,
combine_SI = combine_SI,
confidence_level = confidence_level
),
error = function(e) stop_(gsub("in sir_calc_df(): ", "", e$message, fixed = TRUE), call = -5)
)
}
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